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Multitemporal Images Change Detection Based on AMMF and Spectral Constraint Strategy
- Source :
- IEEE Transactions on Geoscience and Remote Sensing. 59:3444-3457
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Change detection (CD) for multitemporal remote sensing images can find change trends and reveal the development patterns. However, the typical methods based on algebraic operation, image transformation, or segmentation may not yield satisfactory results due to the spectral variability and noise complexity. In a sense, it can be considered that multitemporal images are composed of unchanged and changed regions, as well as noise. In the light of this, a stepwise subtraction method for CD is proposed, based on auto-updating multitemporal matrix factorization (AMMF) and spectral constraint, to remove the unchanged regions and noise from the original images step by step. The unchanged regions are first identified by AMMF, during which the distribution and subspace information of each temporal image are regularized to encode the spatio-temporal correlation. Then, mean shift smoothness is adopted as a spectral constraint so as to remove the noise. In this way, the changed regions have been highlighted so that the change map can be obtained by a postsegmentation method. Experiments have been conducted on three multitemporal data sets, including images from Quick Bird, aerial, and GF-1, indicating that the proposed method is effective and robust, which is superior to some state-of-the-art methods.
- Subjects :
- Computer science
business.industry
Feature extraction
0211 other engineering and technologies
Pattern recognition
02 engineering and technology
Image segmentation
Matrix decomposition
General Earth and Planetary Sciences
Segmentation
Artificial intelligence
Mean-shift
Noise (video)
Electrical and Electronic Engineering
business
Subspace topology
Change detection
021101 geological & geomatics engineering
Subjects
Details
- ISSN :
- 15580644 and 01962892
- Volume :
- 59
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Accession number :
- edsair.doi...........9108d0f2539b465f05fe7f0f86345d1e